Fairness for Affective and Wellbeing Computing

Datasets, algorithms, machine learning models and AI powered tools that are used for perception, prediction and decision making constitute the core of affective and wellbeing computing. Majority of these are prone to data or algorithmic bias (e.g., along the demographic attributes of race, age, gender etc.) that could have catastrophic consequences for various members of the society. Therefore, making considerations and providing solutions to avoid and/or mitigate these are of utmost importance for creating and deploying fair and unbiased affective and wellbeing computing systems. This talk will present the Cambridge Affective Intelligence and Robotics (AFAR) Lab's (https://cambridge-afar.github.io/) research explorations in this area.

The first part of the talk will discuss the lack of publicly available datasets with consideration for fair distribution across the human population and it will present a systematic investigation of bias and fairness in facial expression recognition and mental health prediction by comparing various approaches on well-known benchmark datasets. The second part of the talk will question whether counterfactuals can provide a solution for data imbalance, and will introduce an attempt to achieve fairer prediction models for facial expression recognition, while noting the limitations of a counterfactual approach employed at the pre-processing, in-processing and post-processing stages to mitigate for bias.

Majority of the ML methods aiming to mitigate bias focus on balancing data distributions or learning to adapt to the imbalances by adjusting the learning algorithm. The third and last part of the talk will introduce our work demonstrating how continual learning (CL) approaches are well-suited for mitigating bias by balancing learning with respect to different attributes such as race and gender, without compromising on recognition accuracy. The talk at its various stages will also outline recommendations to achieve greater fairness for affective and wellbeing computing, while emphasising the need for such models to be deployed and tested in real world settings and applications, for example for robotic wellbeing coaching via physical robots.